July 24, 2021

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Will a lung cancer patient get the 2nd primary lung cancer again?

Will a lung cancer patient get the 2nd primary lung cancer again?

Will a lung cancer patient get the 2nd primary lung cancer again?

 

 

Will a lung cancer patient get the 2nd primary lung cancer again? JNCI: Will a second primary lung cancer occur in patients with lung cancer? Predictive model is here. 

 

With the advancement of treatment methods, the number of lung cancer survivors is increasing rapidly, and more and more evidence shows that lung cancer survivors are at high risk of second primary lung cancer (SPLC).

 

Recently, in a study published in the Journal of the National Cancer Institute, researchers developed and verified an SPLC prediction model based on a large number of populations. This prediction tool can help identify high-risk lung cancer patients with SPLC, which can be included in SPLC monitoring and Clinical decision-making for screening.

 

Will a lung cancer patient get the 2nd primary lung cancer again?

Website screenshot

 

Researchers used data from 6325 former smokers in a multi-ethnic cohort (MEC) diagnosed with primary lung cancer (IPLC) from 1993 to 2017, and used cause-specific Cox regression analysis to develop a prediction of SPLC risk 10 years after IPLC diagnosis model. The clinical utility of the model was then evaluated using decision curve analysis, and it was externally verified using two population-based data PLCO and NLST. The two cohorts included 2963 and 2844 IPLCs (101 and 93 SPLC cases), respectively.

 

The results of the study showed that 145 (2.3%) patients in the MEC cohort developed SPLC during the 14,063 person-years. Bootstrap verification based on the MEC cohort shows that the prediction model has high prediction accuracy (Brier score=2.9, 95%CI 2.4-3.3) and discrimination (AUC=81.9%, 95%CI 78.2%-85.5%).

 

The stratification of the estimated risk quartile showed that the difference in the incidence of SPLC observed in the fourth quartile compared to the first quartile was statistically significant (9.5% vs 0.2%, P<0.001).

 

The decision curve analysis shows that within the 10-year risk threshold range of 1%-20%, the model produces greater net benefits than the hypothetical full-screening or no-screening scenarios. In the external verification using PLCO and NLST, the AUC was 78.8% (95%CI 74.6%-82.9%) and 72.7% (95%CI 67.7%-77.7%), respectively.

 

The model can be used for free at https://splc-riskprediction.shinyapps.io/SPLC-RiskAssessmentTool/.

 

(source:internet, reference only)


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